{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MovieLens的数据集--数据探索\n",
    "\n",
    "数据集包含三个主要文件：\n",
    "1、用户电影打分三元表：u.data\n",
    "4个字段：\n",
    "1)user_id:用户ID\n",
    "2)movie_id:电影ID\n",
    "3)rating:用户打分\n",
    "4)timestamp:表示用户打分的时间，unix seconds since 1/1/1970 UTC\n",
    "    \n",
    "2、用户基本信息表：u.user\n",
    "共5个字段：\n",
    "1)user_id:用户ID\n",
    "2)age:年龄\n",
    "3)gender:性别\n",
    "4)occupation:职业\n",
    "5)sip_code:邮编\n",
    "    \n",
    "3、电影基本信息表:u.item\n",
    "共5+19个字段：\n",
    "1)movie_id:电影ID\n",
    "2)title:电影标题(带年份)\n",
    "3)release_data:电影发布日期\n",
    "4)video_release_data:Video发布日期\n",
    "5)imdb_url:链接\n",
    "\n",
    "6-25)genres(共19维):\n",
    "'unknown','Action','Adventure'\n",
    "'Animation','Children\\'s','Crime','Documentary','Drama','Fantasy',\n",
    "'Filem-Noir','Horror','Musical','Mystery','Romance','Sci-Fi','Thriller','War','Western'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
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       "      <td>891717742</td>\n",
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       "      <th>2</th>\n",
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       "      <td>884182806</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>881171488</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>253</td>\n",
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       "      <td>5</td>\n",
       "      <td>891628467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>305</td>\n",
       "      <td>451</td>\n",
       "      <td>3</td>\n",
       "      <td>886324817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "      <td>883603013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742\n",
       "2       22      377       1  878887116\n",
       "3      244       51       2  880606923\n",
       "4      166      346       1  886397596\n",
       "5      298      474       4  884182806\n",
       "6      115      265       2  881171488\n",
       "7      253      465       5  891628467\n",
       "8      305      451       3  886324817\n",
       "9        6       86       3  883603013"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读入评分数据\n",
    "triplet_cols=['user_id', 'item_id', 'rating', 'timestamp']#设置表头\n",
    "\n",
    "dpath='./datas/'\n",
    "#数据分隔符是tab键(sep='\\t')\n",
    "df_triplet=pd.read_csv(dpath+'u.data', sep='\\t', names=triplet_cols, encoding='latin-1')\n",
    "df_triplet.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 4 columns):\n",
      "user_id      100000 non-null int64\n",
      "item_id      100000 non-null int64\n",
      "rating       100000 non-null int64\n",
      "timestamp    100000 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 3.1 MB\n"
     ]
    }
   ],
   "source": [
    "#数据基本情况\n",
    "df_triplet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>1997-12-04 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
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       "      <td>1998-04-05 03:22:22</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1997-11-27 13:02:03</td>\n",
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       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1998-02-02 13:33:16</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   user_id  item_id  rating           timestamp\n",
       "0      196      242       3 1997-12-04 23:55:49\n",
       "1      186      302       3 1998-04-05 03:22:22\n",
       "2       22      377       1 1997-11-07 15:18:36\n",
       "3      244       51       2 1997-11-27 13:02:03\n",
       "4      166      346       1 1998-02-02 13:33:16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_triplet['timestamp'].astype('float64')\n",
    "df_triplet['timestamp']=df_triplet['timestamp'].map(datetime.datetime.fromtimestamp) # 时间格式转换，变换成datetime格式\n",
    "df_triplet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算用户数、电影数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users = 943\n",
      "Number of movies = 1682\n"
     ]
    }
   ],
   "source": [
    "n_users = df_triplet['user_id'].unique().shape[0]#查看该列(user_id)有多少个不同的元素(shape)\n",
    "n_items = df_triplet['item_id'].unique().shape[0]\n",
    "print ('Number of users = ' + str(n_users) + '\\n'+ 'Number of movies = ' + str(n_items) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算每个用户的评分次数\n",
    "看哪些用户最活跃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405    737\n",
       "655    685\n",
       "13     636\n",
       "450    540\n",
       "276    518\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每部电影的评分人数，可看出电影的流行程度，默认是降序排列\n",
    "#value_counts取不同用户id，统计对应的值\n",
    "user_freq = df_triplet['user_id'].value_counts() \n",
    "user_freq.head()#输出前5条数据，每个id对应的打分次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(user_freq.index, user_freq)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算每个电影的评分次数\n",
    "看哪些电影最流行(被评分次数最多) 基于流行度的推荐可以推荐最流行的电影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>258</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>181</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>294</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_id  rating_times\n",
       "50        50           583\n",
       "258      258           509\n",
       "100      100           508\n",
       "181      181           507\n",
       "294      294           485"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每部电影的评分人数，可看出电影的流行程度，默认是降序排列\n",
    "items_rating_times = df_triplet['item_id'].value_counts() \n",
    "#item_freq.head()\n",
    "\n",
    "df_items_sorted_by_rating_times = pd.DataFrame({'item_id':items_rating_times.index, 'rating_times':items_rating_times})\n",
    "df_items_sorted_by_rating_times.head()#输出前5个电影的信息，每个id，以及该电影对应的评分次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按索引排列。item_id排序\n",
    "plt.bar(items_rating_times.index, items_rating_times)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按评分次数排列，最高的排前面。\n",
    "items_rating_times1 = items_rating_times.copy()\n",
    "items_rating_times1.index = range(items_rating_times1.count()) # 对索引重新赋值，方便画图\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "items_rating_times1.plot(ax=ax, title='Rating Times');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看看最流行的是哪些电影，与u.item关联"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id              title release_date  video_release_date  \\\n",
       "0        1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1        2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2        3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3        4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4        5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   Adventure  Animation  Children's   ...     Fantasy  Film-Noir  Horror  \\\n",
       "0          0          1           1   ...           0          0       0   \n",
       "1          1          0           0   ...           0          0       0   \n",
       "2          0          0           0   ...           0          0       0   \n",
       "3          0          0           0   ...           0          0       0   \n",
       "4          0          0           0   ...           0          0       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0        0       0         0    0        0  \n",
       "1        0        0        0       0         1    0        0  \n",
       "2        0        0        0       0         1    0        0  \n",
       "3        0        0        0       0         0    0        0  \n",
       "4        0        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "item_cols = ['item_id', 'title', 'release_date', 'video_release_date', 'imdb_url', 'unknown', 'Action', 'Adventure',\n",
    " 'Animation', 'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
    " 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western'] \n",
    "\n",
    "dpath = './datas/'\n",
    "df_items = pd.read_csv(dpath +'u.item', sep='|', names=item_cols,encoding='latin-1') \n",
    "df_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating_times</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>...</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>ranking_rating_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>583</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "      <td>01-Jan-1977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Star%20Wars%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>258</td>\n",
       "      <td>509</td>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>11-Jul-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?Contact+(1997/I)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>508</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>14-Feb-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Fargo%20(1996)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>507</td>\n",
       "      <td>Return of the Jedi (1983)</td>\n",
       "      <td>14-Mar-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Return%20of%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294</td>\n",
       "      <td>485</td>\n",
       "      <td>Liar Liar (1997)</td>\n",
       "      <td>21-Mar-1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?Liar+Liar+(1997)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  rating_times                      title release_date  \\\n",
       "0       50           583           Star Wars (1977)  01-Jan-1977   \n",
       "1      258           509             Contact (1997)  11-Jul-1997   \n",
       "2      100           508               Fargo (1996)  14-Feb-1997   \n",
       "3      181           507  Return of the Jedi (1983)  14-Mar-1997   \n",
       "4      294           485           Liar Liar (1997)  21-Mar-1997   \n",
       "\n",
       "   video_release_date                                           imdb_url  \\\n",
       "0                 NaN  http://us.imdb.com/M/title-exact?Star%20Wars%2...   \n",
       "1                 NaN          http://us.imdb.com/Title?Contact+(1997/I)   \n",
       "2                 NaN    http://us.imdb.com/M/title-exact?Fargo%20(1996)   \n",
       "3                 NaN  http://us.imdb.com/M/title-exact?Return%20of%2...   \n",
       "4                 NaN          http://us.imdb.com/Title?Liar+Liar+(1997)   \n",
       "\n",
       "   unknown  Action  Adventure  Animation          ...           Film-Noir  \\\n",
       "0        0       1          1          0          ...                   0   \n",
       "1        0       0          0          0          ...                   0   \n",
       "2        0       0          0          0          ...                   0   \n",
       "3        0       1          1          0          ...                   0   \n",
       "4        0       0          0          0          ...                   0   \n",
       "\n",
       "   Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \\\n",
       "0       0        0        0        1       1         0    1        0   \n",
       "1       0        0        0        0       1         0    0        0   \n",
       "2       0        0        0        0       0         1    0        0   \n",
       "3       0        0        0        1       1         0    1        0   \n",
       "4       0        0        0        0       0         0    0        0   \n",
       "\n",
       "   ranking_rating_times  \n",
       "0                     0  \n",
       "1                     1  \n",
       "2                     2  \n",
       "3                     3  \n",
       "4                     4  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据频次大小依次取电影信息。打分次数排序表和电影元素进行关联\n",
    "df_items_sorted_by_rating_times_merge = pd.merge(df_items_sorted_by_rating_times, df_items, how='left', left_on='item_id', right_on='item_id')\n",
    "#添加字段ranking_rating_times\n",
    "df_items_sorted_by_rating_times_merge['ranking_rating_times']=range(items_rating_times.count()) # 加上排名,数字越小，排在越前面\n",
    "\n",
    "df_items_sorted_by_rating_times_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 前二十大流行电影\n",
    "popular_items_count_top_20 = df_items_sorted_by_rating_times_merge.iloc[0:20]['rating_times']#电影打分次数\n",
    "popular_items_top_20_titles =  df_items_sorted_by_rating_times_merge.iloc[0:20]['title']#电影标题\n",
    "\n",
    "objects = (list(popular_items_top_20_titles))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_items_count_top_20)\n",
    " \n",
    "plt.rcdefaults()    \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Rating Count')\n",
    "plt.title('Most popular Movies')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算每个电影的受欢迎程度\n",
    "看哪些电影的平均评分分数最高（总评分？） 基于受欢迎的程度可以推荐最受欢迎的电影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>mean_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>1293</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>1467</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>1653</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>814</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>1122</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_id  mean_rating\n",
       "item_id                      \n",
       "1293        1293          5.0\n",
       "1467        1467          5.0\n",
       "1653        1653          5.0\n",
       "814          814          5.0\n",
       "1122        1122          5.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_mean_rating = df_triplet['rating'].groupby(df_triplet['item_id']).mean()#以电影id分组，求每组打分均值\n",
    "items_mean_rating = items_mean_rating.sort_values(ascending = False)#打分降序排列\n",
    "\n",
    "#组成DataFrame可视化\n",
    "df_items_sorted_by_mean_rating = pd.DataFrame({'item_id':items_mean_rating.index, 'mean_rating':items_mean_rating})\n",
    "df_items_sorted_by_mean_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/qianzhihe/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3296: FutureWarning: 'item_id' is both an index level and a column label.\n",
      "Defaulting to column, but this will raise an ambiguity error in a future version\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>item_id</th>\n",
       "      <th>mean_rating</th>\n",
       "      <th>rating_times</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
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       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
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       "      <th>ranking_rating_times</th>\n",
       "      <th>ranking_mean_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>16-Jan-1998</td>\n",
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       "      <td>1467</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>Saint of Fort Washington, The (1993)</td>\n",
       "      <td>01-Jan-1993</td>\n",
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       "      <td>27-Sep-1996</td>\n",
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       "      <th>3</th>\n",
       "      <td>814</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Great Day in Harlem, A (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Great%20Day%2...</td>\n",
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       "      <td>1122</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>They Made Me a Criminal (1939)</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  mean_rating  rating_times  \\\n",
       "0     1293          5.0             3   \n",
       "1     1467          5.0             2   \n",
       "2     1653          5.0             1   \n",
       "3      814          5.0             1   \n",
       "4     1122          5.0             1   \n",
       "\n",
       "                                               title release_date  \\\n",
       "0                                    Star Kid (1997)  16-Jan-1998   \n",
       "1               Saint of Fort Washington, The (1993)  01-Jan-1993   \n",
       "2  Entertaining Angels: The Dorothy Day Story (1996)  27-Sep-1996   \n",
       "3                      Great Day in Harlem, A (1994)  01-Jan-1994   \n",
       "4                     They Made Me a Criminal (1939)  01-Jan-1939   \n",
       "\n",
       "   video_release_date                                           imdb_url  \\\n",
       "0                 NaN  http://us.imdb.com/M/title-exact?imdb-title-12...   \n",
       "1                 NaN  http://us.imdb.com/M/title-exact?Saint%20of%20...   \n",
       "2                 NaN  http://us.imdb.com/M/title-exact?Entertaining%...   \n",
       "3                 NaN  http://us.imdb.com/M/title-exact?Great%20Day%2...   \n",
       "4                 NaN  http://us.imdb.com/M/title-exact?They%20Made%2...   \n",
       "\n",
       "   unknown  Action  Adventure        ...          Horror  Musical  Mystery  \\\n",
       "0        0       0          1        ...               0        0        0   \n",
       "1        0       0          0        ...               0        0        0   \n",
       "2        0       0          0        ...               0        0        0   \n",
       "3        0       0          0        ...               0        0        0   \n",
       "4        0       0          0        ...               0        0        0   \n",
       "\n",
       "   Romance  Sci-Fi  Thriller  War  Western  ranking_rating_times  \\\n",
       "0        0       1         0    0        0                  1450   \n",
       "1        0       0         0    0        0                  1483   \n",
       "2        0       0         0    0        0                  1652   \n",
       "3        0       0         0    0        0                  1661   \n",
       "4        0       0         0    0        0                  1615   \n",
       "\n",
       "   ranking_mean_rate  \n",
       "0                  0  \n",
       "1                  1  \n",
       "2                  2  \n",
       "3                  3  \n",
       "4                  4  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据频次大小依次取电影信息。关联\n",
    "df_items_sorted_by_mean_rating_merge = pd.merge(df_items_sorted_by_mean_rating, df_items_sorted_by_rating_times_merge, how='left', left_on='item_id', right_on='item_id')\n",
    "df_items_sorted_by_mean_rating_merge['ranking_mean_rate']=range(items_mean_rating.count()) # 加上排名,数字越小，排在越前面\n",
    "\n",
    "df_items_sorted_by_mean_rating_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "原来这些全 5 分的电影都只有 1-3 个评分！这就把排名排上去了！ 看来平均分不靠谱，得把评分人次也考虑进去！（总评分？）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 前二十大流行电影\n",
    "popular_items_count_top_20 = df_items_sorted_by_mean_rating_merge.iloc[0:20]['mean_rating']\n",
    "popular_items_top_20_titles =  df_items_sorted_by_mean_rating_merge.iloc[0:20]['title']\n",
    "\n",
    "objects = (list(popular_items_top_20_titles))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_items_count_top_20)\n",
    " \n",
    "plt.rcdefaults()    \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Mean Rating')\n",
    "plt.title('Most popular Movies')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>mean_rating</th>\n",
       "      <th>rating_times</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>ranking_rating_times</th>\n",
       "      <th>ranking_mean_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>408</td>\n",
       "      <td>4.491071</td>\n",
       "      <td>112</td>\n",
       "      <td>Close Shave, A (1995)</td>\n",
       "      <td>28-Apr-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Close%20Shave...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>305</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>318</td>\n",
       "      <td>4.466443</td>\n",
       "      <td>298</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>01-Jan-1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Schindler's%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>169</td>\n",
       "      <td>4.466102</td>\n",
       "      <td>118</td>\n",
       "      <td>Wrong Trousers, The (1993)</td>\n",
       "      <td>01-Jan-1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Wrong%20Trous...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>286</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>483</td>\n",
       "      <td>4.456790</td>\n",
       "      <td>243</td>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>01-Jan-1942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Casablanca%20...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>114</td>\n",
       "      <td>4.447761</td>\n",
       "      <td>67</td>\n",
       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
       "      <td>05-Apr-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/Title?Wallace+%26+Gromit%3A...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>483</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>64</td>\n",
       "      <td>4.445230</td>\n",
       "      <td>283</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Shawshank%20R...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>603</td>\n",
       "      <td>4.387560</td>\n",
       "      <td>209</td>\n",
       "      <td>Rear Window (1954)</td>\n",
       "      <td>01-Jan-1954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Rear%20Window...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12</td>\n",
       "      <td>4.385768</td>\n",
       "      <td>267</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>14-Aug-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Usual%20Suspe...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>50</td>\n",
       "      <td>4.358491</td>\n",
       "      <td>583</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "      <td>01-Jan-1977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Star%20Wars%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>178</td>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
       "      <td>12 Angry Men (1957)</td>\n",
       "      <td>01-Jan-1957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?12%20Angry%20...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>263</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_id  mean_rating  rating_times  \\\n",
       "15      408     4.491071           112   \n",
       "16      318     4.466443           298   \n",
       "17      169     4.466102           118   \n",
       "18      483     4.456790           243   \n",
       "19      114     4.447761            67   \n",
       "20       64     4.445230           283   \n",
       "21      603     4.387560           209   \n",
       "22       12     4.385768           267   \n",
       "23       50     4.358491           583   \n",
       "24      178     4.344000           125   \n",
       "\n",
       "                                                title release_date  \\\n",
       "15                              Close Shave, A (1995)  28-Apr-1996   \n",
       "16                            Schindler's List (1993)  01-Jan-1993   \n",
       "17                         Wrong Trousers, The (1993)  01-Jan-1993   \n",
       "18                                  Casablanca (1942)  01-Jan-1942   \n",
       "19  Wallace & Gromit: The Best of Aardman Animatio...  05-Apr-1996   \n",
       "20                   Shawshank Redemption, The (1994)  01-Jan-1994   \n",
       "21                                 Rear Window (1954)  01-Jan-1954   \n",
       "22                         Usual Suspects, The (1995)  14-Aug-1995   \n",
       "23                                   Star Wars (1977)  01-Jan-1977   \n",
       "24                                12 Angry Men (1957)  01-Jan-1957   \n",
       "\n",
       "    video_release_date                                           imdb_url  \\\n",
       "15                 NaN  http://us.imdb.com/M/title-exact?Close%20Shave...   \n",
       "16                 NaN  http://us.imdb.com/M/title-exact?Schindler's%2...   \n",
       "17                 NaN  http://us.imdb.com/M/title-exact?Wrong%20Trous...   \n",
       "18                 NaN  http://us.imdb.com/M/title-exact?Casablanca%20...   \n",
       "19                 NaN  http://us.imdb.com/Title?Wallace+%26+Gromit%3A...   \n",
       "20                 NaN  http://us.imdb.com/M/title-exact?Shawshank%20R...   \n",
       "21                 NaN  http://us.imdb.com/M/title-exact?Rear%20Window...   \n",
       "22                 NaN  http://us.imdb.com/M/title-exact?Usual%20Suspe...   \n",
       "23                 NaN  http://us.imdb.com/M/title-exact?Star%20Wars%2...   \n",
       "24                 NaN  http://us.imdb.com/M/title-exact?12%20Angry%20...   \n",
       "\n",
       "    unknown  Action  Adventure        ...          Horror  Musical  Mystery  \\\n",
       "15        0       0          0        ...               0        0        0   \n",
       "16        0       0          0        ...               0        0        0   \n",
       "17        0       0          0        ...               0        0        0   \n",
       "18        0       0          0        ...               0        0        0   \n",
       "19        0       0          0        ...               0        0        0   \n",
       "20        0       0          0        ...               0        0        0   \n",
       "21        0       0          0        ...               0        0        1   \n",
       "22        0       0          0        ...               0        0        0   \n",
       "23        0       1          1        ...               0        0        0   \n",
       "24        0       0          0        ...               0        0        0   \n",
       "\n",
       "    Romance  Sci-Fi  Thriller  War  Western  ranking_rating_times  \\\n",
       "15        0       0         1    0        0                   305   \n",
       "16        0       0         0    1        0                    34   \n",
       "17        0       0         0    0        0                   286   \n",
       "18        1       0         0    1        0                    74   \n",
       "19        0       0         0    0        0                   483   \n",
       "20        0       0         0    0        0                    46   \n",
       "21        0       0         1    0        0                   106   \n",
       "22        0       0         1    0        0                    55   \n",
       "23        1       1         0    1        0                     0   \n",
       "24        0       0         0    0        0                   263   \n",
       "\n",
       "    ranking_mean_rate  \n",
       "15                 15  \n",
       "16                 16  \n",
       "17                 17  \n",
       "18                 18  \n",
       "19                 19  \n",
       "20                 20  \n",
       "21                 21  \n",
       "22                 22  \n",
       "23                 23  \n",
       "24                 24  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去掉评分次数<20次的电影\n",
    "df_items_sorted_by_mean_rating_merge2 = df_items_sorted_by_mean_rating_merge[df_items_sorted_by_mean_rating_merge.rating_times>20]\n",
    "df_items_sorted_by_mean_rating_merge2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 前二十大流行电影\n",
    "popular_items_count_top_20 = df_items_sorted_by_mean_rating_merge2.iloc[0:20]['mean_rating']\n",
    "popular_items_top_20_titles =  df_items_sorted_by_mean_rating_merge2.iloc[0:20]['title']\n",
    "\n",
    "objects = (list(popular_items_top_20_titles))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(popular_items_count_top_20)\n",
    " \n",
    "plt.rcdefaults()    \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Mean Rating')\n",
    "plt.title('Most popular songs')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评分次数与平均评分的相关性\n",
    "散点图scatter\n",
    "横轴是打分次数，纵轴是平均打分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "ax.scatter(df_items_sorted_by_mean_rating_merge['rating_times'],df_items_sorted_by_mean_rating_merge['mean_rating']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可见，评分少的不见得就平均分就低，从整体趋势来看，评分次数多的，平均分也高。可见，流行电影确实受人欢迎。从另一个角度看，存在不少广受大众欢迎的电影，但也存在不少看的人不多，但评分很高质量很好的电影，太流行的电影肯定大家都看过了，关键是如何找到那些还比较小众的电影，这些电影可能具备大众欢迎的元素，但因宣传做得不好没被大众发现，也可能是这些电影就是小众，在小圈子里非常受欢迎，但到更大的人群中就不行。如何把这些电影推荐给何时的人，是个性化推荐要考虑的问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算电影的年份（从title中来）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>mean_rating</th>\n",
       "      <th>rating_times</th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>...</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>ranking_rating_times</th>\n",
       "      <th>ranking_mean_rate</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1293</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Star Kid (1997)</td>\n",
       "      <td>16-Jan-1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?imdb-title-12...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1450</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1467</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Saint of Fort Washington, The (1993)</td>\n",
       "      <td>01-Jan-1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Saint%20of%20...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1483</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1653</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Entertaining Angels: The Dorothy Day Story (1996)</td>\n",
       "      <td>27-Sep-1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Entertaining%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1652</td>\n",
       "      <td>2</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>814</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Great Day in Harlem, A (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Great%20Day%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1661</td>\n",
       "      <td>3</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1122</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>They Made Me a Criminal (1939)</td>\n",
       "      <td>01-Jan-1939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?They%20Made%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1615</td>\n",
       "      <td>4</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  mean_rating  rating_times  \\\n",
       "0     1293          5.0             3   \n",
       "1     1467          5.0             2   \n",
       "2     1653          5.0             1   \n",
       "3      814          5.0             1   \n",
       "4     1122          5.0             1   \n",
       "\n",
       "                                               title release_date  \\\n",
       "0                                    Star Kid (1997)  16-Jan-1998   \n",
       "1               Saint of Fort Washington, The (1993)  01-Jan-1993   \n",
       "2  Entertaining Angels: The Dorothy Day Story (1996)  27-Sep-1996   \n",
       "3                      Great Day in Harlem, A (1994)  01-Jan-1994   \n",
       "4                     They Made Me a Criminal (1939)  01-Jan-1939   \n",
       "\n",
       "   video_release_date                                           imdb_url  \\\n",
       "0                 NaN  http://us.imdb.com/M/title-exact?imdb-title-12...   \n",
       "1                 NaN  http://us.imdb.com/M/title-exact?Saint%20of%20...   \n",
       "2                 NaN  http://us.imdb.com/M/title-exact?Entertaining%...   \n",
       "3                 NaN  http://us.imdb.com/M/title-exact?Great%20Day%2...   \n",
       "4                 NaN  http://us.imdb.com/M/title-exact?They%20Made%2...   \n",
       "\n",
       "   unknown  Action  Adventure  ...   Musical  Mystery  Romance  Sci-Fi  \\\n",
       "0        0       0          1  ...         0        0        0       1   \n",
       "1        0       0          0  ...         0        0        0       0   \n",
       "2        0       0          0  ...         0        0        0       0   \n",
       "3        0       0          0  ...         0        0        0       0   \n",
       "4        0       0          0  ...         0        0        0       0   \n",
       "\n",
       "   Thriller  War  Western  ranking_rating_times  ranking_mean_rate  year  \n",
       "0         0    0        0                  1450                  0  1997  \n",
       "1         0    0        0                  1483                  1  1993  \n",
       "2         0    0        0                  1652                  2  1996  \n",
       "3         0    0        0                  1661                  3  1994  \n",
       "4         0    0        0                  1615                  4  1939  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用正则表达式取出上映年份\n",
    "df_items_sorted_by_mean_rating_merge['year'] = df_items_sorted_by_mean_rating_merge.title.str.extract('(\\((\\d{4})\\))', expand=True).iloc[:,1] \n",
    "df_items_sorted_by_mean_rating_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#去掉年份后的Title\n",
    "#import re\n",
    "#pattern = re.compile(r'^(.*)\\((\\d+)\\)$')\n",
    "#title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(df_items_sorted_by_mean_rating_merge['title']))}\n",
    "#df_items_sorted_by_mean_rating_merge['title'] = df_items_sorted_by_mean_rating_merge['title'].map(title_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#统计每年上映的电影数目，默认是降序排列\n",
    "items_sorted_by_year = df_items_sorted_by_mean_rating_merge['year'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22884d5f98>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z3HzzzY3XI0aMaPz74MGDmTdvXjo6OvLUU0/lpZdeytVXX51TTjklX/7yl4/BlgAAAADg7Q0qjK1Zs2bA63vuuSft7e3ZtGlTLrnkksb7I0aMSEdHx2Hv8eijj+a5557LY489lrFjx+aCCy7ILbfckhtvvDHLly/P8OHDj2AbAAAAADA4R/WMsd7e3iRJW1vbgPfvu+++vO9978v555+fpUuX5n/+538aYxs3bszUqVMzduzYxntdXV3p6+vL1q1bD/s9+/btS19f34ALAAAAAI7GoE6M/aJDhw7lC1/4Qj784Q/n/PPPb7z/6U9/OmeddVbGjx+fZ599NjfeeGO2bduWf/u3f0uS9PT0DIhiSRqve3p6DvtdK1euzIoVK450qQAAAADwS444jHV3d+eHP/xhvvvd7w54/7rrrmv8e+rUqRk3blwuu+yybN++PZMmTTqi71q6dGmWLFnSeN3X15eJEyce2cIBAAAAIEf4U8rFixdn9erV+fa3v50JEya85dyLLrooSfLjH/84SdLR0ZFdu3YNmPPG6zd7Lllzc3NaWloGXAAAAABwNAYVxvr7+7N48eI88MADeeKJJ/KBD3zgbT+zefPmJMm4ceOSJJ2dndmyZUt2797dmLNu3bq0tLRkypQpg1kOAAAAAByxQf2Usru7O6tWrcq3vvWtjB49uvFMsNbW1px22mnZvn17Vq1alcsvvzynn356nn322Vx//fW55JJLMm3atCTJ7NmzM2XKlFx11VW59dZb09PTk5tuuind3d1pbm4+9jsEAAAAgMMY1ImxO++8M729vZk5c2bGjRvXuP71X/81STJ8+PA89thjmT17ds4999z8+Z//eebPn5+HHnqocY+hQ4dm9erVGTp0aDo7O/NHf/RHufrqq3PzzTcf250BAAAAwFsY1Imx/v7+txyfOHFi1q9f/7b3Oeuss/Lwww8P5qsBAAAA4Jg6oofvAwAAAMDJThgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKGnegF/CqZeu/Utxzfcs2W47QSAAAAAN6OE2MAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACU5K9Svoc8f+7ktxyf/KPnj9NKAAAAAH71OTEGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlDSqMrVy5MjNmzMjo0aPT3t6eK664Itu2bRsw57XXXkt3d3dOP/30jBo1KvPnz8+uXbsGzNmxY0fmzZuXESNGpL29PTfccEMOHDhw9LsBAAAAgHdoUGFs/fr16e7uzve+972sW7cu+/fvz+zZs7N3797GnOuvvz4PPfRQ7r///qxfvz47d+7MJz7xicb4wYMHM2/evLz++ut56qmncu+99+aee+7JsmXLjt2uAAAAAOBtDBvM5DVr1gx4fc8996S9vT2bNm3KJZdckt7e3vzzP/9zVq1alUsvvTRJcvfdd2fy5Mn53ve+l4svvjiPPvponnvuuTz22GMZO3ZsLrjggtxyyy258cYbs3z58gwfPvzY7Q4AAAAA3sRRPWOst7c3SdLW1pYk2bRpU/bv359Zs2Y15px77rk588wzs3HjxiTJxo0bM3Xq1IwdO7Yxp6urK319fdm6dethv2ffvn3p6+sbcAEAAADA0TjiMHbo0KF84QtfyIc//OGcf/75SZKenp4MHz48Y8aMGTB37Nix6enpacz5xSj2xvgbY4ezcuXKtLa2Nq6JEyce6bIBAAAAIMlRhLHu7u788Ic/zDe/+c1juZ7DWrp0aXp7exvXiy+++K5/JwAAAAC/2gb1jLE3LF68OKtXr86GDRsyYcKExvsdHR15/fXX8+qrrw44NbZr1650dHQ05nz/+98fcL83/mrlG3P+f83NzWlubj6SpQIAAADAYQ3qxFh/f38WL16cBx54IE888UQ+8IEPDBifPn16TjnllDz++OON97Zt25YdO3aks7MzSdLZ2ZktW7Zk9+7djTnr1q1LS0tLpkyZcjR7AQAAAIB3bFAnxrq7u7Nq1ap861vfyujRoxvPBGttbc1pp52W1tbWLFy4MEuWLElbW1taWlry+c9/Pp2dnbn44ouTJLNnz86UKVNy1VVX5dZbb01PT09uuummdHd3OxUGAAAAwHEzqDB25513Jklmzpw54P2777471157bZLk9ttvz5AhQzJ//vzs27cvXV1d+frXv96YO3To0KxevTqLFi1KZ2dnRo4cmWuuuSY333zz0e0EAAAAAAZhUGGsv7//beeceuqpueOOO3LHHXe86ZyzzjorDz/88GC+GgAAAACOqSP+q5QAAAAAcDITxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShLGAAAAAChJGAMAAACgJGEMAAAAgJKEMQAAAABKEsYAAAAAKEkYAwAAAKAkYQwAAACAkoQxAAAAAEoSxgAAAAAoSRgDAAAAoCRhDAAAAICShDEAAAAAShp0GNuwYUM+9rGPZfz48WlqasqDDz44YPzaa69NU1PTgGvOnDkD5rzyyitZsGBBWlpaMmbMmCxcuDB79uw5up0AAAAAwCAMOozt3bs3H/rQh3LHHXe86Zw5c+bkpZdealz/8i//MmB8wYIF2bp1a9atW5fVq1dnw4YNue666wa/egAAAAA4QsMG+4G5c+dm7ty5bzmnubk5HR0dhx17/vnns2bNmjzzzDP5jd/4jSTJ1772tVx++eW57bbbMn78+MEuCQAAAAAG7V15xtiTTz6Z9vb2nHPOOVm0aFFefvnlxtjGjRszZsyYRhRLklmzZmXIkCF5+umnD3u/ffv2pa+vb8AFAAAAAEfjmIexOXPm5Bvf+EYef/zx/NVf/VXWr1+fuXPn5uDBg0mSnp6etLe3D/jMsGHD0tbWlp6ensPec+XKlWltbW1cEydOPNbLBgAAAKCYQf+U8u186lOfavx76tSpmTZtWiZNmpQnn3wyl1122RHdc+nSpVmyZEnjdV9fnzgGAAAAwFF5V35K+Ys++MEP5n3ve19+/OMfJ0k6Ojqye/fuAXMOHDiQV1555U2fS9bc3JyWlpYBFwAAAAAcjXc9jP3Xf/1XXn755YwbNy5J0tnZmVdffTWbNm1qzHniiSdy6NChXHTRRe/2cgAAAAAgyRH8lHLPnj2N019J8h//8R/ZvHlz2tra0tbWlhUrVmT+/Pnp6OjI9u3b8xd/8Rc5++yz09XVlSSZPHly5syZk8985jO56667sn///ixevDif+tSn/EVKAAAAAI6bQZ8Y+8EPfpALL7wwF154YZJkyZIlufDCC7Ns2bIMHTo0zz77bH7/938/v/7rv56FCxdm+vTp+c53vpPm5ubGPe67776ce+65ueyyy3L55ZfnIx/5SP7hH/7h2O0KAAAAAN7GoE+MzZw5M/39/W86vnbt2re9R1tbW1atWjXYrwYAAACAY+Zdf8YYAAAAALwXCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEkkaq5iAAAgAElEQVTCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScNO9AI4tu743BNvOd5916XHaSUAAAAA721OjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQkjAEAAABQkjAGAAAAQEnCGAAAAAAlCWMAAAAAlCSMAQAAAFCSMAYAAABAScIYAAAAACUJYwAAAACUJIwBAAAAUJIwBgAAAEBJwhgAAAAAJQljAAAAAJQ06DC2YcOGfOxjH8v48ePT1NSUBx98cMB4f39/li1blnHjxuW0007LrFmz8sILLwyY88orr2TBggVpaWnJmDFjsnDhwuzZs+fodgIAAAAAgzDoMLZ379586EMfyh133HHY8VtvvTV/+7d/m7vuuitPP/10Ro4cma6urrz22muNOQsWLMjWrVuzbt26rF69Ohs2bMh111135LsAAAAAgEEaNtgPzJ07N3Pnzj3sWH9/f/76r/86N910Uz7+8Y8nSb7xjW/k/7Z352FyVXXC+E91d8hCSBqyxyzsJIhoxJcQEEEWCUQFB3gRYWQYXkGFGREHBtwIjBvCjI4Kw4xCxBFnHNzBeXEgLGOEwBAWE94gZEEIkIhECElIJyTf3x883b80pKuXc5sq6n4+z9PPk+pb59b3e8655976pm7XmDFj0s9+9rP0wQ9+MC1evDjdfPPN6X/+53/SO97xjpRSSt/85jfTMccck6644oo0fvz41+y3ra0ttbW1dTxes2ZNb8MGAAAAgE4K/Rtjy5cvTytXrkxHHHFEx++GDx+epk+fnu6+++6UUkp33313am1t7SiKpZTSEUcckZqamtI999yzzf1++ctfTsOHD+/4mThxYpFhAwAAAFBChRbGVq5cmVJKacyYMZ1+P2bMmI5tK1euTKNHj+60vaWlJe20004dz3m1iy66KL3wwgsdP08++WSRYQMAAABQQr2+lbIWBg4cmAYOHFjrMAAAAABoIIV+Ymzs2LEppZRWrVrV6ferVq3q2DZ27Nj0hz/8odP2l19+Oa1evbrjOQAAAADQ3wotjO2yyy5p7Nixae7cuR2/W7NmTbrnnnvSjBkzUkopzZgxIz3//PNpwYIFHc+57bbb0pYtW9L06dOLDAcAAAAAutTrWynXrl2blixZ0vF4+fLl6cEHH0w77bRTmjRpUjr33HPTF77whbTHHnukXXbZJX3uc59L48ePT8cdd1xKKaWpU6emmTNnpo985CPp6quvTps2bUrnnHNO+uAHP7jNb6QEAAAAgP7Q68LYfffdl9797nd3PD7vvPNSSimddtpp6bvf/W664IIL0rp169KZZ56Znn/++fTOd74z3XzzzWnQoEEdba6//vp0zjnnpMMPPzw1NTWl448/Pn3jG98oIB0AAAAA6JleF8YOPfTQFBFdbq9UKunSSy9Nl156aZfP2WmnndIPfvCD3r40AAAAABSm0L8xBgAAAABvFApjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAACl1FLrAKgvf3/Se6tu/9QPb3qdIgEAAADoXz4xBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKVUeGFs9uzZqVKpdPqZMmVKx/YNGzaks88+O40YMSINHTo0HX/88WnVqlVFhwEAAAAAVfXLJ8be/OY3p2eeeabjZ968eR3bPvnJT6Ybb7wx3XDDDenOO+9MTz/9dPqzP/uz/ggDAAAAALrU0i87bWlJY8eOfc3vX3jhhXTNNdekH/zgB+mwww5LKaU0Z86cNHXq1DR//vx0wAEH9Ec4AAAAAPAa/fKJscceeyyNHz8+7brrrumUU05JTzzxREoppQULFqRNmzalI444ouO5U6ZMSZMmTUp33313l/tra2tLa9as6fQDAAAAADkKL4xNnz49ffe7300333xz+qd/+qe0fPnydPDBB6cXX3wxrVy5Mm233XaptbW1U5sxY8aklStXdrnPL3/5y2n48OEdPxMnTiw6bAAAAABKpvBbKY8++uiOf++7775p+vTpafLkyek//uM/0uDBg/u0z4suuiidd955HY/XrFmjOAYAAABAln65lXJrra2tac8990xLlixJY8eOTRs3bkzPP/98p+esWrVqm3+TrN3AgQPTsGHDOv0AAAAAQI5+L4ytXbs2LV26NI0bNy7tt99+acCAAWnu3Lkd23/3u9+lJ554Is2YMaO/QwEAAACADoXfSvk3f/M36X3ve1+aPHlyevrpp9PFF1+cmpub08knn5yGDx+ezjjjjHTeeeelnXbaKQ0bNiz91V/9VZoxY4ZvpAQAAADgdVV4YWzFihXp5JNPTs8991waNWpUeuc735nmz5+fRo0alVJK6Wtf+1pqampKxx9/fGpra0tHHXVUuuqqq4oOAwAAAACqKrww9u///u9Vtw8aNChdeeWV6corryz6pQEAAACgx/r9b4wBAAAAQD1SGAMAAACglBTGAAAAACglhTEAAAAASklhDAAAAIBSUhgDAAAAoJQUxgAAAAAoJYUxAAAAAEpJYQwAAACAUlIYAwAAAKCUFMYAAAAAKCWFMQAAAABKSWEMAAAAgFJSGAMAAACglBTGAAAAACglhTEAAAAASklhDAAAAIBSUhgDAAAAoJQUxgAAAAAoJYUxAAAAAEpJYQwAAACAUlIYAwAAAKCUFMYAAAAAKKWWWgdAY1lx4a+7fc6Erxz8OkQCAAAAUJ1PjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJSSwhgAAAAApaQwBgAAAEApKYwBAAAAUEoKYwAAAACUksIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlFJLrQOAV5s9e3bW9rm37VZ1++GHLe1lRAAAAEAj8okxAAAAAEpJYQwAAACAUlIYAwAAAKCUFMYAAAAAKCWFMQAAAABKSWEMAAAAgFJSGAMAAACglBTGAAAAACglhTEAAAAASklhDAAAAIBSUhgDAAAAoJQUxgAAAAAoJYUxAAAAAEpJYQwAAACAUlIYAwAAAKCUFMYAAAAAKCWFMQAAAABKSWEMAAAAgFJSGAMAAACglBTGAAAAACglhTEAAAAASklhDAAAAIBSUhgDAAAAoJQUxgAAAAAoJYUxAAAAAEqppdYBQD0ae/uDVbevfPfbqm7f+cJfVt3++Fdm9TomAAAAoFg+MQYAAABAKSmMAQAAAFBKbqWEOuRWTAAAAOh/PjEGAAAAQCkpjAEAAABQSm6lhEY1e3g321+ouvkt172l6vaFpy3sbUQAAABQV3xiDAAAAIBSUhgDAAAAoJQUxgAAAAAoJYUxAAAAAEpJYQwAAACAUvKtlEC/WDxlarfPmfrI4tchEgAAANg2nxgDAAAAoJQUxgAAAAAoJYUxAAAAAEpJYQwAAACAUlIYAwAAAKCUfCslULeu/OhtVbefffVhVbf//Unvrbr9Uz+8qdcxAQAA0Dh8YgwAAACAUlIYAwAAAKCU3EoJUMWKC39ddfuErxxcdfvs2bOzts+9bbeq2w8/bGnV7SmlNPb2B6tuX/nut1XdvvOFv6y6/fGvzOo2BgAAgHrkE2MAAAAAlJLCGAAAAACl5FZKAPpVd7diptSD2zFnD+9m+wtVN7/lurdU3b7wtIXV959SWjxlatXtUx9ZXHW7b1kFAID64xNjAAAAAJSSwhgAAAAApeRWSgAoiXr/ltWUuv+m1Xr4llXf1AoA0Dh8YgwAAACAUlIYAwAAAKCU3EoJAPB68i2r3X7Lakrdf9OqW4Pd1gsARfCJMQAAAABKSWEMAAAAgFJyKyUAAJRU9u2Y/XxrcErd3x7s1uD8W4NT6v724FrfGpyS24OB/uETYwAAAACUksIYAAAAAKXkVkoAAADe8NwanH9rcErd3x7c3a3B8EbjE2MAAAAAlJLCGAAAAACl5FZKAAAAoBD9/S2rPXmOb1mlN3xiDAAAAIBSqmlh7Morr0w777xzGjRoUJo+fXq69957axkOAAAAACVSs1spf/jDH6bzzjsvXX311Wn69Onp61//ejrqqKPS7373uzR69OhahQUAAABQU7X+ltWUuv+m1Vp/y2pKPfum1e7U7BNj//AP/5A+8pGPpNNPPz3tvffe6eqrr05DhgxJ1157ba1CAgAAAKBEavKJsY0bN6YFCxakiy66qON3TU1N6Ygjjkh33333a57f1taW2traOh6/8MIrlc01a9Z0/G5L2/qqr7n1c7vUFtW3d7OPzS9tzoph7ea89iml9NLGdVn72LBpU1b7F9uqv35P9rH1WPel/bp1W7Lap5TSlnVrs/aROx/LMJ97so9az+eUup/T5nN++57so9bzOaX8Ndp87n4+92QftZ7PhezDfO52PvdkH+ZzOeZzT2Iwn/Pnc0r51xz9PZ+L2If53LO50N/vCXPnc0/2YT6n7PmcUu1rHN3N51fvo/3fEd3k/iqV6G2LAjz99NPpTW96U7rrrrvSjBkzOn5/wQUXpDvvvDPdc889nZ4/e/bsdMkll7zeYQIAAADwBvLkk0+mCRMm9Pj5NfsbY71x0UUXpfPOO6/j8ZYtW9Lq1avTiBEjUqVSec3z16xZkyZOnJiefPLJNGzYsD69Zu4+at1eDMW0r4cYGiGHeohBDvURQyPkUA8xyEEMRbWvhxgaIYd6iEEOYiiqfT3E0Ag51EMMchBDUe3rIYaetI+I9OKLL6bx48f3at81KYyNHDkyNTc3p1WrVnX6/apVq9LYsWNf8/yBAwemgQMHdvpda2trt68zbNiwPg96UfuodXsxFNO+HmJohBzqIQY51EcMjZBDPcQgBzEU1b4eYmiEHOohBjmIoaj29RBDI+RQDzHIQQxFta+HGLprP3x4N186sA01+eP72223Xdpvv/3S3LlzO363ZcuWNHfu3E63VgIAAABAf6nZrZTnnXdeOu2009I73vGOtP/++6evf/3rad26den000+vVUgAAAAAlEjz7NmzZ9fihffZZ5/U2tqavvjFL6YrrrgipZTS9ddfn/baa69C9t/c3JwOPfTQ1NLS99pf7j5q3V4MxbSvhxgaIYd6iEEO9RFDI+RQDzHIQQxFta+HGBohh3qIQQ5iKKp9PcTQCDnUQwxyEENR7eshhiJy2JaafCslAAAAANRaTf7GGAAAAADUmsIYAAAAAKWkMAYAAABAKSmMAQAAAFBKCmMAAAAAlJLCGAAAAACl1Dx79uzZtQ6iHm3atCktW7YstbS0pEGDBvV5P6effnraf//90w477FD1eQsWLEjjx4/v8+u027x5c2pq+v/rnffcc09atmxZGj9+fGpubs7e/+th5cqVae7cuenOO+9MCxYsSE899VRqbW1NQ4cOrXVor4ui5kIjWLduXZo/f36aP39+Wrx4cdqwYUMaN25cqlQqPWq/efPm9Pjjj6fhw4enSqWS2tra0k9+8pO0cOHCNGrUqB7NqSLm48svv5wWLlyYHnroobR06dK0bt26NGLEiD4fkz1dV7pyySWXpH322ScNGTKk2+f+8Y9/7NHzupM7llsran3uTT+kVMw49tca/fLLL6cVK1ak4cOH9/j5c+fOTXfccUdavXp1mjx5cqe4qik6h02bNvU596LmQq6cHF6tt2NZVPveHg85ilpXctbnos+1OcdUUetjX2MoYm0rco1PqXfHVO65vqj5+GqrVq1Kq1ev7vOxXITc47qIta2n/VDEOBRx3deoenPtWHQ/5qyPW6uHY+r1jqHI9xBFjcPW+8u5Xqk70YD+9Kc/xb/8y7/EZz/72fj2t78dzz//fNXnX3bZZbF+/fqIiHj55ZfjU5/6VGy33XbR1NQULS0tcfrpp8fGjRur7uOhhx7a5s+AAQPipz/9acfjrlQqldhtt93ii1/8Yjz11FO9zvnpp5+Ogw46KJqbm+Nd73pXrF69OmbNmhWVSiUqlUrsueee8fTTT3e7n1WrVsXcuXM7+mzlypVx2WWXxZe//OX47W9/W7Xtxo0b4/zzz4/ddtst/tf/+l9xzTXXdNq+cuXKaGpq6rL92rVr45RTTonm5uZoaWmJ0aNHx+jRo6OlpSWam5vj1FNPjXXr1vWgN7q2du3auPPOO7t93tKlS+O6666Lr3zlK/HVr341fvSjH8ULL7zQp9fs7XzMnQvd6Ukf5I5lRN5c2rx5c5x//vkxZMiQaGpqiqampo65PHny5PjFL37RbZ4PPfRQjBs3LpqammKfffaJJ554IvbZZ5/YfvvtY+jQobHjjjvGvffe22X7Iubj5s2b4zOf+Uy0trZ2xN/+09raGp/97Gdj8+bNVXPIWVdeeOGF1/w8//zzMWDAgLjnnns6fldNU1NTHHbYYXH99dfHhg0bqj63qz7IGcsi1ufcfsgdx4ji1uiuPPjgg1WPyXPOOSduvPHGiIh48sknY8qUKdHc3BxjxoyJ5ubmeMtb3hIrVqzo1xx++MMfRltbW8fjb37zmzFp0qRoamqKESNGxCWXXFL19YuYCxF5a1NuDj3R3Vjmti9iXcg9R+SuK0Wsz7nn2iKOqdz1MTeGIta23Bxyj6ncc31E/nxcs2ZNnHLKKTFp0qT48Ic/HG1tbfHxj388KpVKNDU1xbve9a4eX0P29foz97guYm3L7YfccShiLkREXHnllXH44YfHiSeeGLfeemunbc8++2zssssu/dq+mp5cw+deOxbRj7lrU5HHVETv34sVFUPOXChifS7iPFVNT69X6vmY2lpDFMY+8IEPxA033BAREYsWLYqRI0fGqFGjYvr06TFmzJgYO3Zs/L//9/+6bN/U1BSrVq2KiIjLL788dtxxx7j22mvj4Ycfju9///sxevTouOyyy6rG0H6QvHribv37ahOnUqnERz7ykY4Lu1mzZsVPf/rTePnll3vUB3/+538eBx54YPziF7+Ik046KQ488MA4+OCDY8WKFfH73/8+DjrooDj77LOr7uP222+P7bffPiqVSowdOzYefPDBmDBhQuyxxx6x1157xcCBA+NXv/pVl+0vvvjiGDNmTFx++eXxmc98JoYPHx5nnnlmx/aVK1dGpVLpsv0ZZ5wRe+yxR9x8882d8n755ZfjV7/6Vey5557xf/7P/+lRf3SluwN47dq1ccIJJ3Qau7Fjx0Zzc3MMHTo0vvWtb3X7GrnzMXcudKcni1juWObOpb/927+NqVOnxo033hi33HJLvOtd74rLLrssFi9eHJ/73Oe6bR8RcdRRR8UJJ5wQCxcujE984hMxderUOPHEE2Pjxo2xadOmOPXUU+OII47osn0R8/H888+PUaNGxdVXXx3Lly+P9evXx/r162P58uXxz//8zzF69Oi44IILumyfu660v0l59U9P27fHMHPmzNhuu+1ixx13jHPOOSceeOCBqm22ljuWRazPuf2QO44RxazR1XR3XI8ZMyYWLlwYERH/+3//7zjiiCPi2WefjYiI5557Lt773vfGCSec0K85bD2W1157bQwaNCg+//nPxy9/+cv4whe+ENtvv318+9vf7lH7vs6F3LUpN4ee6O/CWBHrQu45InddKWJ9zj3XFnFM5a6PuTEUsbYVucb35ZjKPddH5M/Hc845J6ZMmRLf+MY34tBDD41jjz029tlnn5g3b17ceeedsffee8enP/3pqvvIvf7MPa6LWNty+yF3HIqYC//4j/8YQ4YMibPPPjtOPfXU2G677eJLX/pSx/buiv657bvTk/ND7rVjEf2YuzblzqXc92JFxJA7F4pYn4s4T1XTk/lY78fU1hqiMLbjjjvG4sWLIyLi6KOPjg996EMd/+uxcePGOOOMM+I973lPl+0rlUrHyWDatGnxz//8z522f//73483v/nNVWN461vfGrNmzYrFixfH448/Ho8//ngsX748Wlpa4pZbbun4XXcxbNq0KX70ox/FMccc01HRveCCC+J3v/td1dcfN25c3H333RHxykSvVCqdKqpz586NXXfdteo+3vnOd8bZZ58dL774Ylx++eXxpje9qdObnL/5m7+JAw88sMv2u+++e0dVOiLisccei9133z3+4i/+IrZs2dLtxG1tbY3f/OY3XW6fN29etLa2Vs2hO90dwGeeeWYcdNBBsXDhwnjsscfihBNOiAsuuCDWrVsX11xzTQwZMiSuv/76qq9R1Hzs61zoTk8WsdyxzJ1L48aNi//+7//ueLxixYoYOnRox/8gXnrppTFjxoyqOey4444dJ73169dHc3Nz3HPPPR3bFy1aFCNGjOiyfRHzccyYMXHzzTd3uf3mm2+O0aNHd7k9d11505veFLNmzYrbbrst7rjjjrjjjjvi9ttvj+bm5pgzZ07H76ppn4/PPvtsXHHFFbH33ntHU1NTvP3tb4+rrrqq2/8tyx3LItbn3H7IHceI/DV62rRpVX+mTJlS9ZgcNGhQLFu2LCIiJkyY0OlYiIhYuHBhjBw5sl9z2Hos999///jqV7/aaftVV10V06ZN61H7vs6F3LUpN4f22HPGMrd9EetC7jkid10pYrEVxHMAAB5BSURBVH3OPdcWdUzlrI+5MRS1thW1xvflmMo9128dQ1/n48SJE+O2226LiIinnnoqKpVKp+Pjpptuir322qvqPnKvP3OP6yLWttx+yB2HIubC3nvv3amff/Ob38SoUaPic5/7XER0/yY8t313enINn3vtWEQ/5q5NuXMp971YETHkzoUi1ufccci93oio/2Nqaw1RGBs8eHAsWbIkIl45Qd9///2dtv/ud7+L4cOHd9m+UqnEH/7wh4iIGDFiREdltd2yZctiyJAhVWNoa2uLT3ziE7H33nt3ev2WlpZ4+OGHu81h6xNSuxUrVsSll14au+66azQ1NcXBBx/cZftBgwbFE0880fF4++23j8cee6zj8e9///sYPHhw1RiGDRvW0Y+bNm2KlpaWTv9T8+ijj1btx8GDB8fy5ctfk8Oee+4Zp5xySjz11FNVJ+6wYcPif/7nf7rcfu+998awYcOq5rDjjjtW/Rk2bFjVGEaOHBn33Xdfx+PVq1fHoEGDOm7J+Na3vhVve9vbqsZQxHzMmQu5fdCeQ+5Y5sylHXbYIZYuXdrxePPmzdHS0hLPPPNMREQ8/PDD3R6Tra2t8eijj0bEKyfB5ubmWLBgQcf2xYsXx4477lg1h9z5OGTIkKq3Zj300EOx/fbbd7k9d1157rnn4rjjjot3v/vdnT4q3dP2Eduej3fddVf85V/+Zeywww4xZMiQ+PM///Mu2+eOZRHrc24/5I5jRP4aPXDgwDjttNNi9uzZ2/w566yzqh6T++67b/z7v/97RERMnTo1brnllk7b77rrrthpp536NYetx3LkyJHx4IMPdtq+ZMmS2GGHHXrUvq9zIXdtys0hIn8sc9sXsS7kniNy15Ui1ufcc20Rx1Tu+pgbQxFrW5FrfF+OqdxzfXsMOfNx4MCBndbGIUOGdCqsPv74492uTbnXn7nHdVFrW04/5I5DEXNhW2vbwoULY8yYMXHhhRd2+yY8t30R1/C5145F9GPu2pQ7l3LfixUVQ85cKGJ9LmIccq43Imp/TPVGQxTGpk+fHv/yL/8SEa9UNn/605922v5f//VfMXbs2C7bVyqV+OIXvxj/+I//GOPGjXvNvdsPPfRQtwtAu//8z/+MCRMmxJe+9KWOi4OeLEJbf4R5W2699db40Ic+1OX2SZMmdaoC/+3f/m0899xzHY8ffPDBbv/ncuTIkbFo0aKIiFi3bl00NTV1fDog4pV+qLaPXXbZ5TX3/Ua8UmXfc88948gjj6w6cT/0oQ/FtGnTXrN4RUTcf//9sd9++8Upp5xSNYchQ4bEpz71qfjud7+7zZ9LLrmk20+ttZ8MIl45IbS0tHRcLDz66KMxaNCgqjHkzsfcuZDbBxH5Y5k7lw488MD4whe+0PH43/7t3zr97//ChQu7PSYPP/zwOOOMM2LFihVxySWXxO677x6nn356x/aPf/zjVd/0FDEfjznmmHjPe97T8bHlrT377LMxc+bMmDVrVtV9RPR9XWl31VVXxfjx4+MHP/hBRPTuDXC1+bh27dr4zne+U/UTNrljWeT63Nd+KGIcc9fo/fbbL6666qoutz/wwANVj8k5c+bEhAkT4vbbb4/vfe97MXXq1Lj11lvjqaeeittuuy3e8pa3dHvrWW4OlUolvve978XPf/7zmDBhQtx1112dti9atKhqMaOIuZC7NuXmEJE/lrnt2+WsC7nniNx1pYj1OfdcW8Qxlbs+5sZQxNpWxBqfc0zlnusj8ufj+PHjOxUOTj755E77W7RoUbdrUxHXnxF9P66LWNty+yF3HIqYCxMnTuz0Cch2Dz/8cIwZMyY+/OEPV13bctsXcQ3frq/XjkX0Y+7alDuXct+LFRFD7lwoYn3OHYcirjdqfUz1RkMUxm666abYaaedYs6cOTFnzpzYeeed4zvf+U785je/iWuvvTYmTpwY559/fpftJ0+eHDvvvHPHz9e+9rVO27/+9a/HAQcc0ON4Vq5cGUcffXQcfPDBWZ8Y6433v//98fWvf73L7d/61rfisMMOq7qPY489Nt773vfGvHnz4swzz4x3vOMdMWvWrFi7dm2sW7cuTjjhhJg5c2aX7c8444z4y7/8y21uW7FiRey+++5VJ+7q1atj5syZUalUYqeddoopU6bElClTYqeddoqmpqY4+uij409/+lPVHA488MCq/dDdR5CPPPLITrfVXH755TFu3LiOx/fff3+3Bcbc+Zg7F3L7ICJ/LHPn0q233hoDBw6M/fffP971rndFS0tLp+Py8ssv73Y+33vvvTFixIhoamqKUaNGxaJFi2L69OkxduzYGD9+fAwePHibb+zaFTEf2/9gaUtLS0ybNi1mzpwZM2fOjGnTpkVLS0vsu+++nf43qpq+rCtbe/jhh+Otb31rnHzyydmfGOuN3LEsen3uSz8UMY65a/Rf//Vfxyc+8Ykuty9ZsiQOPfTQqjH8/d//fQwZMiQGDx7c8Ufr23+OO+64ePHFF/s1h1f/rZOt30xHRHznO9+peqtOEXMhd23KzSEifyyLmAvt+rou5J4jcteVItbn3Bgi8o+pIs51OTEUsbbl5pB7TOWe69tjyJkLM2fOjKuvvrrL7XPmzKla0Iko5vqzXV+O6yLWttx+yB2HIubCySefHOeee+42ty1atChGjRpVdW3LbV/ENfzW+nLtWEQ/RuStTblzKfe9WBEx5M6Fot5D5IxDEdcbtT6meqMSEVHrb8Yswo9//ON07rnnpqeffjptndLAgQPTRz/60XTFFVf0+euG58+fnwYOHJimTZvWq3bf+MY30u23356++c1vpgkTJlR97p133pkOOuig1NLS0qcYu3PvvfemIUOGpH322afL5zz22GNp1qxZacmSJWnKlCnplltuSR//+MfTf/7nf6aUUtpxxx3TzTffnN7+9rdvs/3vf//79Mgjj6Sjjjpqm9uffvrpdMstt6TTTjutaqyLFy9O8+fPTytXrkwppTR27Ng0Y8aMNGXKlG7z/NKXvpQ2bdqULr744m1uf/LJJ9PnP//5NGfOnG1uv//++9ORRx6Ztttuu7TddtullStXpuuuuy598IMfTCmldOWVV6Z77703XXfddVXjyJmPuXMhtw9Syh/L3LmUUkoPPfRQ+o//+I/U1taWjjrqqHTkkUd2+dyurFu3Lj3yyCNpr732SkOHDk0bNmxI119/fXrppZfSkUcemfbaa69u95EzH1NKacuWLelXv/rVNvfxnve8p9dfk9ybdeXVNm7cmC688MJ0++23p5/85Cdpl1126bZN+/wfOHBgr15ra0WMZVf6sj73pR+KHsdX68kaXYTnn38+3XLLLWnZsmVpy5Ytady4cemggw5Ke+yxR/a+c3O46aab0oABA7pcd7rTk7lQxNrUnznUQl+Oh9xzRBHrSkopPfLII+nuu+/u0/pc1HVX7jFVxPqYE0MRa1t/rvE9OaZyz/W583H16tWpqakptba2bnP7//2//zcNHjw4HXrooV3uo6jrz3Z9Oa6r6ck45PZDEetC7lz47W9/mxYsWJBOP/30bW5ftGhR+vGPf9zlNXZu+yKu4belt9eORVw/p9T3tamIYyq3NpAbQ+5cSKm4a8/+vPbrTq2Pqd5omMJYSilt3rw5LViwIC1fvrxj0Pfbb7+0ww471Dq0N5TnnnsujRgxouPx3Llz00svvZRmzJjR6feN6plnnkk33XRTamtrS4cddljae++9+7SfzZs3p/vvv7/TIlS2+Vj2uQTUJ2sTUG+Kuv4EXuG9GL3RUIWxXLfddluaN29eeuaZZ1JTU1Padddd0/vf//5eVVNfvY/ddtstve997+vxPnJjKCKHXI2QQ6099NBDacGCBenQQw9Nu+66a3r44YfTlVdembZs2ZI+8IEPvC6fRqhlDD/+8Y/T0UcfnYYMGVLofpcvX56WLFmSxo0b16NPtfRXHxx22GFpzpw5afLkyf0aQ730Y0Skxx9/PE2cODG1tLSkjRs3pp/+9Kepra0tHXPMMWnkyJFV2/fHOPQ2h9z2bW1tqampKQ0YMCCllNLSpUvTtddem5544ok0efLkdMYZZ1T9H/3csSxqLhQ9Fr3px9w+LEJ/HVMp9W5deLXc+dzb1++vsehpDEW8fn/kUMQ4vJ5jWS/zOeccUVQOueepbck5pvui1jnUw3kqtw/645go4pjujVqvz+1yx6I/5nOu1/tcW1QfbNmyZZufLtuyZUtasWJFmjRpUili6FYhN2TWiblz58Yll1wSH/3oR+PjH/94XHHFFZ3+kGVXVq1aFfvvv380NTVFS0tLNDU1xX777Rdjx46N5ubmbu9BLmIftW6/tVr1Yz3kEPHK/fvXXHNNx9fbLlq0KD72sY/FWWedVfVrc7e1j/ZvaurNPn784x9Hc3NzjBgxIoYOHRq33HJLtLa2xhFHHBFHHXVUNDc3V/3K7tzXr4cYKpVKDBs2LD7ykY/E/Pnzu413Wz72sY913De/fv36OP7446OpqSkqlUo0NTXFu9/97qr31RfRBz//+c+3+dPc3Bzf+ta3Oh73Vwz10I+PPPJITJ48OSqVSuy+++6xbNmy2G+//WL77bePIUOGxMiRI6sem0WMw7ZyaP87Kj3JIbd9RMQhhxwSN9xwQ0REzJs3LwYOHBj77rtvnHTSSTFt2rQYMmTIa/7Y8dZyx7KIuZA7FrlzKbcPIyI2bNgQGzdu7Hi8ZMmS+PSnPx2nnnpqfOYzn+lY97tSRD/mrgu5/Zj7+hH5Y5EbQxFzIXcfRawLtR7LepjP7eeIpqamPp0jisghN4Yijqnctakecqj1eSq3D4qIIfeYzp0HEbVfnyPyxyK3fRH9WOv1uYj5/MILL8SJJ54YgwYNitGjR8fnPve5ePnllzu2d/eNjvUSw6RJk7Ji6KmGKIzlFlROOumkOO644+KFF16IDRs2xDnnnBMf/vCHI+KVAsuIESOq/iHEIvZR6/YRte/HesihiDfhuft4+9vf3vFHT9u/3enSSy/t2H7FFVdU/cruInKodQyVSiUuvfTSmDZtWlQqlXjzm98cX/va1+KPf/xj1bi3tvW3G1100UUxYcKEuO2222LdunUxb9682G233eLCCy/stz5oz6P9JNrVT7WTQW4M9dCPxx57bLz//e+P3/72t3HuuefG1KlT49hjj42NGzfGhg0b4n3ve1+ceuqp/dYHReSQ2z4iYtiwYR0n7kMOOSQ++clPdtr+2c9+Ng466KAu2+eOZRFzIXcscvsxtw/b2+UWKHP7MXddyO3H3NePKGY+58RQxFzI3UcR60Ktx7Ie5nPuOaKIHIqIIfeYyl2b6iGHWp+ncvugiBhyj+kiiv61Xp8j8scit30R/Vjr9bmI+fzXf/3Xseeee8YNN9wQ3/72t2Py5Mkxa9asaGtri4hXilKVSqXhY+iphiiM5RZUhg0b1vH17RGvfCXwgAED4oUXXoiIiH/913+Nvfbaq2oMufuodfuI2vdjPeRQxJvw3H1sv/32sXz58oiI2LJlSwwYMCB++9vfdmxfunRpDB06tF9zqHUMW38z0X333Rcf+9jHorW1NQYOHBgnnnhi/Nd//VfV+F+9j3322afjq8vb/fznP48999yzy/a5fRARHV+l/OpvWerpNwPlxlAP/Thq1Kh44IEHIuKVY7pSqcSvf/3rju2/+c1vYtKkSV22L2IccnPIbd+ex+LFiyMiYsyYMfHggw922r5kyZJ+Hcsi5kKR87Gvx2ROH0YU84Yhtx9z14Xcfsx9/Yj8sShibcydC0Uek31dF2o9lvUwn3PPEUXkkBtDEcdU7tpUDznU+jyV2wdF59CXY7qIon+t1+eI/LHIbV9EP9Z6fS5iPk+aNCluv/32jsfPPvts7L///vGe97wnNmzY0O2ntRolhp5qiMJYbkFl1KhRnSbo+vXro6mpKZ577rmIeOVif+DAgVVjyN1HrdtH1L4f6yGHIt6E5+5j7Nixcd9990XEK19JX6lUOi0o9957b4wdO7Zfc6h1DFufjNq99NJL8b3vfS8OPfTQaGpqip133rlqDpVKJf7whz9ERMTIkSM7zYuIiMcffzwGDx7cZfvcPmj3D//wDzFx4sS48cYbO37X05Nibgz10I+DBw+O3//+9x2Phw4dGkuWLOl4/MQTT1Q9rosYh9wccttHRBx22GHx1a9+NSJe+Tr26667rtP2H/3oRz1+89euN2NZxFwoYj7m9GNuH0YUWwxp19t+jMhbF4qYjzmvH1HMWOTEUMTrF3FM5o5DrceyHuZz7jmiiBxyY4jIP6Zy16Z6yKHW56ki+qCIHHKO6SKK/rVenyPyxyK3fRH9WOv1uYj5PHjw4NfcNrpmzZqYMWNGHHbYYbFs2bKqRalGiaGnGqIwlltQ+cAHPhDHH398rF27NjZu3Bjnnntu7L777h3b58+f3+0br9x91Lp9RO37sR5yKOJNeO4+Tj311Jg+fXp8//vfj/e9731x1FFHxQEHHBCLFy+ORx55JA455JA44YQT+jWHWsew9ceXt+Wxxx6LT3/601VzqFQqcdZZZ8UnP/nJGD169Gv+l2/BggUxcuTILtvn9sHWHnjggdh7773jzDPPjHXr1vX4pJgbQz3042677dbpf3auuuqqWLNmTaf2/Xk8FJFDbvuIiLvuuiuGDx8eF198cXzzm9+MkSNHxmc/+9m4/vrr4/Of/3y0trbGZZdd1mX73LEsYi7kjkVuP+b2YUT+G4Yi+rFdX9eFIuZjzutHFDMWOTEU8fq5+yhiHGo9lvUwn3PPEUXkkBtDu5xjKndtqoccan2eKqIPcmPIPaaLKGrVen2OyB+L3PZF9GOt1+ci5vNee+0Vv/zlL1/z+xdffDFmzJgRb33rW6sWpRolhp5qiMJYbkFl6dKlsdtuu0VLS0sMGDAgWltb45ZbbunYPmfOnG7/TkTuPmrdPqL2/VgPORTxJjx3HytXrowjjzwyhg4dGkcddVQ8//zzcc4553Tci77HHnt0qpT3Rw61jmFb/2PXW4ccckgceuihHT/f/va3O23/u7/7uzjkkEO6bJ/bB6+2fv36OOuss2KPPfaI5ubmHp0Uc2Ooh34866yzXtNma1/+8pfjmGOO6XJ7EeOQm0Nu+3Z33XVXHHDAAa/5GxNvetObuv37ibljWcRcyB2LIvoxpw/b2+cWQ3L7cWt9WReKmo99ff12uWORG0MRr5+zjyLGodZjWQ/zOfccUUQOuTFsLWc+56xN9ZBDrc9TRfRBbgy5x3RRRa1ar8+5Y5Hbvoh+rPX6XMR8/qu/+qsu32+tWbMmpk+fXrUo1Sgx9FRDFMaKKKisW7cufvWrX8WNN94Yzz77bJ/iyN1HrdvXQz/WOoci3oQXXVDZOreFCxfGpk2bavL6r2cMjz/+eGzZsqVPMfbU0qVL48knn+xTu570QVd+/vOfx7nnnpt14dXTGOq5H9stW7Ysnn766T69bs44vHpfOTn0tv0f/vCHmD9/ftx1110dtxx3J3cs+3MuFDUWvenHvvRhu5w3DP3Vj0WsC+36Mp9zXj9nLIqIoYjXLyqHreWuK33dR2/68Y0wn7s7R7we57m+nKf60gdFFTO25fXIoZ7PUxE964P+jqEnx3SR86DW63NX+nrt15v2/Xk8Rbz+59pX60kfrF69+jW3gG5tzZo1cccddzR8DD1ViYhIDWD9+vVp3rx5aePGjemAAw5II0eOrHVIb0iN0I/9kcOyZcvS+vXr05QpU1JLS0vN9pGj1q9fLzEApJTSs88+m5YtW5a2bNmSxo0bl3beeedahwRgbSKlZB4URT/SUw1TGMv10ksvpX/7t39L8+bNS88880xqampKu+66azruuOPS4Ycf/rrso9bti9AIOdSDeuiHWsfQCMdkPcQgBzHIoVj6sT5iaIQc6iEGOdRPDLnqIYdax9AIORShHnKohxhy1TqHRhmHeoihRwr53FkdWL9+fVxzzTVx+umnx8yZM+OYY46Jc845J2699dZu2z722GMxefLkGD16dEycODEqlUrMmjUrpk+fHs3NzXHiiSd2e4tI7j5q3b5dLfuxHnIoon3uPoroh9wcah1DIxyT9RCDHMQgh9cq+9okBzHIof5iiHjjX4PXOoZGyCGi9tfw+rE+cmiUcaiHGHqqIQpjuR129NFHx1lnndVxT/lXvvKVOProoyMi4tFHH42dd945Lr744qox5O6j1u0jat+P9ZBDPSwAuf1QRA61jqERjsl6iEEOYpBDZ9YmOYhBDvUYQ63Xpkbox0bIoR6u4fVjfeTQCONQLzH0VEMUxnI7bMiQIfHoo492PG5ra4sBAwbEH//4x4iI+NnPfhY777xz1Rhy91Hr9hG178d6yKEeFoDcfigih1rH0AjHZD3EIAcxyKEza5McxCCHeoyh1mtTI/RjI+RQD9fw+rE+cmiEcaiXGHqqIQpjuR02fvz4WLBgQcfjP/3pT1GpVGLNmjUR8cq3HQwcOLBqDLn7qHX7iNr3Yz3kUA8LQG4/FJFDrWNohGOyHmKQgxjk0Jm1SQ5ikEM9xlDrtakR+rERcqiHa3j9WB85NMI41EsMPdVU3F8rq53W1tb04osvdjxev359evnll9N2222XUkpp3333Tc8880yX7Y888sh03nnnpUceeSQtX748ffSjH01ve9vb0g477JBSSumJJ55Io0ePrhpD7j5q3T6l2vdjPeSQ276IfeT2QxE51DqGRjgm6yEGOYhBDp1Zm+QgBjnUYwy1XpsaoR8bIYd6uIbXj/WRQyOMQ73E0GOFlNdq7LTTTotDDjkkFi9eHMuWLYuTTjoppk2b1rH9jjvuiIkTJ3bZftWqVXHAAQdEpVKJpqammDx5ctx///0d22+44Yb4xje+UTWG3H3Uun1E7fuxHnLIbV/EPnL7oYgcah1DIxyT9RCDHMQgh86sTXIQgxzqMYZar02N0I+NkEM9XMPrx/rIoRHGoV5i6KmGKIwV1WGPPvpoLFy4MOubDXL3Ucv29dKPtcyhHhaAdn3thyIXkFrH0AjHZD3EIAcxFNW+HmJohPNcEfuodft6iKERcqiHGORQ+xjqZW16o/djEe1rGUM9XMMX1b6WMTRSP76Rx6HeYuhOQxTG2vVXhz3xxBNx+umn13Qfr2f7eu3H1zOHelgAutLTfujPBaTWMTTCMVkPMchBDEW1r4cYGuE8V8Q+at2+HmJohBzqIQY5vP4x1Ova9Ebrx/5o/3rGUA/X8P3V/vWMoZH78Y00DvUeQ7uGKox1JbfDHnzwwWhqasqKIXcftW4fUft+rIcc6mEByO2HInKodQyNcEzWQwxyEENR7eshhno4R+jH+oihEXKohxjkUD8x1HptaoR+bIQc6uEaXj8WE0Ot24vhtVqK+Utl9W316tXpuuuuS9dee+02t//iF7+o2n7ZsmXdvkbuPmrdvif6ux/rIYf+bt+TffR3P/Qkh1rH0AjHZD3EIAcxFNW+HmKoh3OEfqyPGBohh3qIQQ71E0N33gjX4LWOoRFy6M7rcQ2vH4uJodbtxdB7lYiIwvZWIz3psE996lNp8+bN29ze1NSUKpVKqtYVlUqly/ZF7KPW7VOqfT/WQw657YvYR24/FJFDrWNohGOyHmKQgxiKal8PMdTDOUI/1kcMjZBDPcQgh/qJodZrUyP0YyPkUA/X8PqxmBhq3V4MfVDEx85qrf0P61UqlS5/qn3Ebvz48fGzn/2sy+0PPPBAtx/Ry91HrdtH1L4f6yGH3PZF7CO3H4rIodYxNMIxWQ8xyEEMRbWvhxjq4RyhH+sjhkbIoR5ikEP9xFDrtakR+rERcqiHa3j9WB85NMI41EsMPdWUX1qrvXHjxqWf/OQnacuWLdv8uf/++6u232+//dKCBQu63N5dlbKIfdS6fUq178d6yCG3fRH7yO2HInKodQyNcEzWQwxyEENR7eshhno4R+jH+oihEXKohxjkUD8x1HptaoR+bIQc6uEaXj/WRw6NMA71EkNPNcTfGGvvsGOPPXab27vrsPPPPz+tW7euy+277757uv3226vGkLuPWrdPqfb9WA855LYvYh+5/VBEDrWOoRGOyXqIQQ5iKKp9PcRQD+cI/VgfMTRCDvUQgxzqJ4Zar02N0I+NkEM9XMPrx2JiqHV7MfReQ/yNsV//+tdp3bp1aebMmdvcvm7dunTfffelQw455HWO7I2lEfoxN4ci+qDW/Vjr16+XGABezdoE1CNrEymZB0XRj/RFQxTGAAAAAKC3GuJvjAEAAABAbymMAQAAAFBKCmMAAAAAlJLCGAAAAAClpDAGAAAAQCkpjAEAAABQSgpjAAAAAJTS/wfj+VZVNwHnhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 12))\n",
    "items_sorted_by_year.plot(kind='bar', title='freq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "协同过滤数据准备\n",
    "\n",
    "对训练数据：\n",
    "1、建立用户和物品索引，方便用下标访问打分表\n",
    "2、建立倒排表、加速查询访问\n",
    "3、打分表按用户、物品索引保存为稀疏矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#字典、用于建立用户和物品的索引\n",
    "from collections import defaultdict\n",
    "\n",
    "#稀疏矩阵、存储打分表\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "\n",
    "#数据到文件存储\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>874965758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>876893171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>878542960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>876893119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>889751712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  rating  timestamp\n",
       "0        1        1       5  874965758\n",
       "1        1        2       3  876893171\n",
       "2        1        3       4  878542960\n",
       "3        1        4       3  876893119\n",
       "4        1        5       3  889751712"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取训练数据\n",
    "triplet_cols=['user_id', 'item_id', 'rating', 'timestamp']\n",
    "\n",
    "df_triplet=pd.read_csv(dpath+'u1.base', sep='\\t', names=triplet_cols, encoding='latin-1')\n",
    "df_triplet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#统计总的用户数目和物品数目\n",
    "unique_users=df_triplet['user_id'].unique()\n",
    "unique_items=df_triplet['item_id'].unique()\n",
    "\n",
    "n_users=unique_users.shape[0]\n",
    "n_items=unique_items.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#建立用户和物品的索引表\n",
    "#本数据集中user_id和item_id都已经是索引了，可以减1，将从1开始编码变成从0开始编码\n",
    "#下面的代码更通用，可对任意编码的用户和物品重新索引\n",
    "users_index=dict()\n",
    "items_index=dict()\n",
    "\n",
    "for j, u in enumerate(unique_users):\n",
    "    users_index[u]=j\n",
    "\n",
    "for j ,i in enumerate(unique_items):\n",
    "    items_index[i]=j\n",
    "\n",
    "#保存用户索引表\n",
    "pickle.dump(users_index, open(dpath+\"users_index.pk1\", 'wb'))\n",
    "pickle.dump(items_index, open(dpath+\"items_index.pk1\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#倒排表\n",
    "#统计每个用户打过分的电影\\每个电影被哪些用户打分\n",
    "user_items=defaultdict(set)\n",
    "item_users=defaultdict(set)\n",
    "\n",
    "#用户-物品关系矩阵R，稀疏矩阵，记录用户对每个电影的打分\n",
    "user_item_scores=ss.dok_matrix((n_users, n_items))\n",
    "\n",
    "#扫描训练数据\n",
    "for line in df_triplet.index:#对每条记录\n",
    "    cur_user_index=users_index[df_triplet.iloc[line]['user_id']]\n",
    "    cur_item_index=items_index[df_triplet.iloc[line]['item_id']]\n",
    "    \n",
    "    #建立倒排表\n",
    "    user_items[cur_user_index].add(cur_item_index)#该用户对这个电影进行了打分\n",
    "    item_users[cur_item_index].add(cur_user_index)#该电影被该用户打分\n",
    "    \n",
    "    user_item_scores[cur_user_index, cur_item_index]=df_triplet.iloc[line]['rating']\n",
    "\n",
    "#保存倒排表\n",
    "#每个用户打分的电影\n",
    "pickle.dump(user_items, open(dpath+\"user_items.pk1\", 'wb'))\n",
    "#对每个电影打过分的用户\n",
    "pickle.dump(item_users, open(dpath+\"item_users.pk1\", 'wb'))\n",
    "\n",
    "#保存打分矩阵，在UserCF和itemCF中用到\n",
    "sio.mmwrite(dpath+\"user_item_scores\", user_item_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于用户的协同过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#距离\n",
    "import scipy.spatial.distance as ssd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读入训练数据\n",
    "users_index=pickle.load(open(dpath+\"users_index.pk1\", 'rb'))\n",
    "items_index=pickle.load(open(dpath+\"items_index.pk1\", 'rb'))\n",
    "\n",
    "n_users=len(users_index)\n",
    "n_items=len(items_index)\n",
    "\n",
    "#倒排表\n",
    "#每个用户打过分的电影\n",
    "user_items=pickle.load(open(dpath+\"user_items.pk1\", 'rb'))\n",
    "#对每个电影打过分的用户\n",
    "item_users=pickle.load(open(dpath+\"item_users.pk1\", 'rb'))\n",
    "\n",
    "#用户-物品关系矩阵R。打分表，稀疏矩阵转换成csr形式，方便后面使用下标访问\n",
    "user_item_scores=sio.mmread(dpath+\"user_item_scores\")\n",
    "user_item_scores=user_item_scores.tocsr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算每个用户的平均打分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_mu=np.zeros(n_users)\n",
    "for u in range(n_users):\n",
    "    n_user_items=0\n",
    "    r_acc=0.0\n",
    "    \n",
    "    for i in user_items[u]:#用户打过分的item\n",
    "        r_acc+=user_item_scores[u, i]\n",
    "        n_user_items+=1\n",
    "        \n",
    "    users_mu[u]=r_acc/n_user_items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算两个用户之间的相似度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_similarity(uid1, uid2):\n",
    "    si={}#有效item（两个用户均有打分的item）的集合\n",
    "    for item in user_items[uid1]:#uid1所有打过分的item1\n",
    "        if item in user_items[uid2]:#如果uid2也对该item打过分\n",
    "            si[item]=1#item为一个有效item\n",
    "    \n",
    "    n=len(si)#有效item数，有效item为即对uid1对item打过分，uid2也对item打过分\n",
    "    if(n==0):#没有共同打过分的item，相似度假设为0\n",
    "        similarity=0.0 \n",
    "        return similarity\n",
    "    \n",
    "    #用户uid1的有效打分（减去该用户的平均打分）\n",
    "    s1=np.array([user_item_scores[uid1, item]-users_mu[uid1] for item in si])\n",
    "    \n",
    "    #用户uid2的有效打分（减去该用户的平均打分）\n",
    "    s2=np.array([user_item_scores[uid2,item]-users_mu[uid2] for item in si])\n",
    "    \n",
    "    similarity=1-ssd.cosine(s1, s2)\n",
    "    \n",
    "    #判空\n",
    "    if np.isnan(similarity):#s1或者s2的l2模为0（全部等于该用户的平均打分）\n",
    "        similarity=0.0 \n",
    "    return similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预计算好所有用户之间的相似性\n",
    "对用户比较少、用户比较固定的的系统适用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ui=0 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/qianzhihe/.local/lib/python3.6/site-packages/scipy/spatial/distance.py:698: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = 1.0 - uv / np.sqrt(uu * vv)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ui=100 \n",
      "ui=200 \n",
      "ui=300 \n",
      "ui=400 \n",
      "ui=500 \n",
      "ui=600 \n",
      "ui=700 \n",
      "ui=800 \n",
      "ui=900 \n"
     ]
    }
   ],
   "source": [
    "users_similarity_matrix = np.matrix(np.zeros(shape=(n_users, n_users)), float)#用户数*用户数的矩阵，对称矩阵\n",
    "\n",
    "for ui in range(n_users):\n",
    "    users_similarity_matrix[ui,ui] = 1.0#自己对自己的相似度最大，为1\n",
    "    \n",
    "    #打印进度条\n",
    "    if(ui % 100 == 0):\n",
    "        print (\"ui=%d \" % (ui))\n",
    "    \n",
    "    #计算第i个用户与第j个用户的相似度\n",
    "    for uj in range(ui+1,n_users):   \n",
    "        users_similarity_matrix[uj,ui] = user_similarity(ui, uj)\n",
    "        users_similarity_matrix[ui,uj] = users_similarity_matrix[uj,ui]\n",
    "\n",
    "#相似度矩阵存起来\n",
    "pickle.dump(users_similarity_matrix, open(dpath+\"users_similarity.pkl\", 'wb')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def users_similarity(n_users ):\n",
    "    users_similarity_matrix = np.matrix(np.zeros(shape=(n_users, n_users)), float)\n",
    "\n",
    "    for ui in range(n_users):\n",
    "        users_similarity_matrix[ui,ui] = 1.0\n",
    "    \n",
    "        #打印进度条\n",
    "        if(ui % 100 == 0):\n",
    "            print (\"ui=:%d \" % (ui))\n",
    "\n",
    "        for uj in range(ui+1,n_users):   \n",
    "            users_similarity_matrix[uj,ui] = user_similarity(ui, uj)\n",
    "            users_similarity_matrix[ui,uj] = users_similarity_matrix[uj,ui]\n",
    "\n",
    "    pickle.dump(users_similarity_matrix, open(dpath+\"users_similarity.pkl\", 'wb')) \n",
    "    return users_similarity_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试\n",
    "预测用户对item的打分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 预测用户对item的打分\n",
    "def User_CF_pred(uid, iid): \n",
    "    sim_accumulate=0.0  \n",
    "    rat_acc=0.0 \n",
    "    for user_id in item_users[iid]:  #对item iid打过分的所有用户\n",
    "        #计算当前用户与给item i打过分的用户之间的相似度\n",
    "        #sim = user_similarity(user_id, uid)\n",
    "        sim = users_similarity_matrix[user_id,uid]\n",
    "            \n",
    "        if sim != 0: \n",
    "            rat_acc += sim * (user_item_scores[user_id,iid] - users_mu[user_id])   #用户user对item i的打分\n",
    "            sim_accumulate += np.abs(sim)  \n",
    "        \n",
    "    if sim_accumulate != 0:  \n",
    "        score = users_mu[uid] + rat_acc/sim_accumulate\n",
    "    else: #no similar users,return average rates of the user \n",
    "        score = users_mu[uid]\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对给定用户，推荐物品/计算打分\n",
    "不同的推荐算法，只是预测打分函数不同， user_items_scores[i] = User_CF_pred(cur_user_id, i) #预测打分\n",
    "\n",
    "如User_CF_pred, Item_CF_pred, svd_CF_pred,... 甚至基于内容的推荐也是一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#user：用户\n",
    "#返回推荐items及其打分（DataFrame）\n",
    "def recommend(user):\n",
    "    cur_user_id = users_index[user]\n",
    "    \n",
    "    #训练集中该用户打过分的item\n",
    "    cur_user_items = user_items[cur_user_id]\n",
    "\n",
    "    #该用户对所有item的打分\n",
    "    user_items_scores = np.zeros(n_items)\n",
    "\n",
    "    #预测打分\n",
    "    for i in range(n_items):  # all items \n",
    "        if i not in cur_user_items: #训练集中没打过分\n",
    "            user_items_scores[i] = User_CF_pred(cur_user_id, i)  #预测打分\n",
    "    \n",
    "    #推荐\n",
    "    #Sort the indices of user_item_scores based upon their value，Also maintain the corresponding score\n",
    "    sort_index = sorted(((e,i) for i,e in enumerate(list(user_items_scores))), reverse=True)\n",
    "    \n",
    "    #Create a dataframe from the following\n",
    "    columns = ['item_id', 'score']\n",
    "    df = pd.DataFrame(columns=columns)\n",
    "         \n",
    "    #Fill the dataframe with top 20 (n_rec_items) item based recommendations\n",
    "    #sort_index = sort_index[0:n_rec_items]\n",
    "    #Fill the dataframe with all items based recommendations\n",
    "    for i in range(0,len(sort_index)):\n",
    "        cur_item_index = sort_index[i][1] \n",
    "        cur_item = list (items_index.keys()) [list (items_index.values()).index (cur_item_index)]\n",
    "            \n",
    "        if ~np.isnan(sort_index[i][0]) and cur_item_index not in cur_user_items:\n",
    "            df.loc[len(df)]=[cur_item, sort_index[i][0]]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取测试数据\n",
    "triplet_cols = ['user_id','item_id', 'rating', 'timestamp'] \n",
    "\n",
    "df_triplet_test = pd.read_csv(dpath +'u1.test', sep='\\t', names=triplet_cols, encoding='latin-1')\n",
    "#df_triplet_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试，并计算评价指标\n",
    "PR、覆盖度、RMSE 这部分代码所有的推荐算法相同\n",
    "\n",
    "令系统的用户集合为 U， R(u) 是根据用户在训练集上的行为给用户作出的推荐列表，而 T(u) 是用户在测试集上的行为列表。那么推荐结果的准确率定义为：\n",
    "𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛=∑𝑢∈𝑈|𝑅(𝑢)∩𝑇(𝑢)|∑𝑢∈𝑈|𝑅(𝑢)|\n",
    " \n",
    "推荐结果的召回率定义为：\n",
    "𝑅𝑒𝑐𝑎𝑙𝑙=∑𝑢∈𝑈|𝑅(𝑢)∩𝑇(𝑢)|∑𝑢∈𝑈|𝑇(𝑢)|\n",
    " \n",
    "推荐系统的覆盖率为：\n",
    "𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒=∑𝑢∈𝑈|𝑅(𝑢)||𝐼|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "599 is a new item.\n",
      "\n",
      "711 is a new item.\n",
      "\n",
      "814 is a new item.\n",
      "\n",
      "830 is a new item.\n",
      "\n",
      "852 is a new item.\n",
      "\n",
      "857 is a new item.\n",
      "\n",
      "1156 is a new item.\n",
      "\n",
      "1236 is a new item.\n",
      "\n",
      "1309 is a new item.\n",
      "\n",
      "1310 is a new item.\n",
      "\n",
      "1320 is a new item.\n",
      "\n",
      "1343 is a new item.\n",
      "\n",
      "1348 is a new item.\n",
      "\n",
      "1364 is a new item.\n",
      "\n",
      "1373 is a new item.\n",
      "\n",
      "1457 is a new item.\n",
      "\n",
      "1458 is a new item.\n",
      "\n",
      "1492 is a new item.\n",
      "\n",
      "1493 is a new item.\n",
      "\n",
      "1498 is a new item.\n",
      "\n",
      "1505 is a new item.\n",
      "\n",
      "1520 is a new item.\n",
      "\n",
      "1533 is a new item.\n",
      "\n",
      "1536 is a new item.\n",
      "\n",
      "1543 is a new item.\n",
      "\n",
      "1557 is a new item.\n",
      "\n",
      "1561 is a new item.\n",
      "\n",
      "1562 is a new item.\n",
      "\n",
      "1563 is a new item.\n",
      "\n",
      "1565 is a new item.\n",
      "\n",
      "1582 is a new item.\n",
      "\n",
      "1586 is a new item.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#统计总的用户\n",
    "unique_users_test = df_triplet_test['user_id'].unique()\n",
    "\n",
    "#为每个用户推荐的item的数目10个\n",
    "n_rec_items = 10\n",
    "\n",
    "#性能评价参数初始化，用户计算Percison和Recall\n",
    "n_hits = 0\n",
    "n_total_rec_items = 0\n",
    "n_test_items = 0\n",
    "\n",
    "#所有被推荐商品的集合（对不同用户），用于计算覆盖度\n",
    "all_rec_items = set()\n",
    "\n",
    "#残差平方和，用与计算RMSE\n",
    "rss_test = 0.0\n",
    "\n",
    "#对每个测试用户\n",
    "for user in unique_users_test:\n",
    "    #测试集中该用户打过分的电影（用于计算评价指标的真实值）\n",
    "    if user not in users_index:   #user在训练集中没有出现过，新用户不能用协同过滤\n",
    "        print(str(user) + ' is a new user.\\n')\n",
    "        continue\n",
    "   \n",
    "    user_records_test= df_triplet_test[df_triplet_test.user_id == user]\n",
    "    \n",
    "    #对每个测试用户，计算该用户对训练集中未出现过的商品的打分，并基于该打分进行推荐（top n_rec_items）\n",
    "    #返回结果为DataFrame\n",
    "    rec_items = recommend(user)\n",
    "    \n",
    "    for i in range(n_rec_items):\n",
    "        item = rec_items.iloc[i]['item_id']\n",
    "        \n",
    "        if item in user_records_test['item_id'].values:\n",
    "            n_hits += 1\n",
    "        all_rec_items.add(item)\n",
    "    \n",
    "    #计算rmse\n",
    "    for i in range(user_records_test.shape[0]):\n",
    "        item = user_records_test.iloc[i]['item_id']\n",
    "        score = user_records_test.iloc[i]['rating']\n",
    "        \n",
    "        df1 = rec_items[rec_items.item_id == item]\n",
    "        if(df1.shape[0] == 0): #item在训练集中没有出现过，新item不能被协同过滤推荐\n",
    "            print(str(item) + ' is a new item.\\n')\n",
    "            continue\n",
    "        pred_score = df1['score'].values[0]\n",
    "        rss_test += (pred_score - score)**2     #残差平方和\n",
    "    \n",
    "    #推荐的item总数\n",
    "    n_total_rec_items += n_rec_items\n",
    "    \n",
    "    #真实item的总数\n",
    "    n_test_items += user_records_test.shape[0]\n",
    "\n",
    "#Precision & Recall\n",
    "precision = n_hits / (1.0*n_total_rec_items)#命中次数除以总的推荐商品中有多少是真的\n",
    "recall = n_hits / (1.0*n_test_items)#推荐的商品中总共占真实值的比例值\n",
    "\n",
    "#覆盖度：推荐商品占总需要推荐商品的比例\n",
    "coverage = len(all_rec_items) / (1.0* n_items)\n",
    "\n",
    "#打分的均方误差\n",
    "rmse=np.sqrt(rss_test / df_triplet_test.shape[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0010893246187363835"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#命中次数除以总的推荐商品中有多少是真的\n",
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00025"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#推荐的商品中总共占真实值的比例值\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10787878787878788"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#覆盖度\n",
    "coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9658669077094278"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#打分的均方误差\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "599 is a new item.\n",
      "\n",
      "711 is a new item.\n",
      "\n",
      "814 is a new item.\n",
      "\n",
      "830 is a new item.\n",
      "\n",
      "852 is a new item.\n",
      "\n",
      "857 is a new item.\n",
      "\n",
      "1156 is a new item.\n",
      "\n",
      "1236 is a new item.\n",
      "\n",
      "1309 is a new item.\n",
      "\n",
      "1310 is a new item.\n",
      "\n",
      "1320 is a new item.\n",
      "\n",
      "1343 is a new item.\n",
      "\n",
      "1348 is a new item.\n",
      "\n",
      "1364 is a new item.\n",
      "\n",
      "1373 is a new item.\n",
      "\n",
      "1457 is a new item.\n",
      "\n",
      "1458 is a new item.\n",
      "\n",
      "1492 is a new item.\n",
      "\n",
      "1493 is a new item.\n",
      "\n",
      "1498 is a new item.\n",
      "\n",
      "1505 is a new item.\n",
      "\n",
      "1520 is a new item.\n",
      "\n",
      "1533 is a new item.\n",
      "\n",
      "1536 is a new item.\n",
      "\n",
      "1543 is a new item.\n",
      "\n",
      "1557 is a new item.\n",
      "\n",
      "1561 is a new item.\n",
      "\n",
      "1562 is a new item.\n",
      "\n",
      "1563 is a new item.\n",
      "\n",
      "1565 is a new item.\n",
      "\n",
      "1582 is a new item.\n",
      "\n",
      "1586 is a new item.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#统计总的用户\n",
    "unique_users_test = df_triplet_test['user_id'].unique()\n",
    "\n",
    "#为每个用户推荐的item的数目20个\n",
    "n_rec_items = 20\n",
    "\n",
    "#性能评价参数初始化，用户计算Percison和Recall\n",
    "n_hits = 0\n",
    "n_total_rec_items = 0\n",
    "n_test_items = 0\n",
    "\n",
    "#所有被推荐商品的集合（对不同用户），用于计算覆盖度\n",
    "all_rec_items = set()\n",
    "\n",
    "#残差平方和，用与计算RMSE\n",
    "rss_test = 0.0\n",
    "\n",
    "#对每个测试用户\n",
    "for user in unique_users_test:\n",
    "    #测试集中该用户打过分的电影（用于计算评价指标的真实值）\n",
    "    if user not in users_index:   #user在训练集中没有出现过，新用户不能用协同过滤\n",
    "        print(str(user) + ' is a new user.\\n')\n",
    "        continue\n",
    "   \n",
    "    user_records_test= df_triplet_test[df_triplet_test.user_id == user]\n",
    "    \n",
    "    #对每个测试用户，计算该用户对训练集中未出现过的商品的打分，并基于该打分进行推荐（top n_rec_items）\n",
    "    #返回结果为DataFrame\n",
    "    rec_items = recommend(user)\n",
    "    \n",
    "    for i in range(n_rec_items):\n",
    "        item = rec_items.iloc[i]['item_id']\n",
    "        \n",
    "        if item in user_records_test['item_id'].values:\n",
    "            n_hits += 1\n",
    "        all_rec_items.add(item)\n",
    "    \n",
    "    #计算rmse\n",
    "    for i in range(user_records_test.shape[0]):\n",
    "        item = user_records_test.iloc[i]['item_id']\n",
    "        score = user_records_test.iloc[i]['rating']\n",
    "        \n",
    "        df1 = rec_items[rec_items.item_id == item]\n",
    "        if(df1.shape[0] == 0): #item在训练集中没有出现过，新item不能被协同过滤推荐\n",
    "            print(str(item) + ' is a new item.\\n')\n",
    "            continue\n",
    "        pred_score = df1['score'].values[0]\n",
    "        rss_test += (pred_score - score)**2     #残差平方和\n",
    "    \n",
    "    #推荐的item总数\n",
    "    n_total_rec_items += n_rec_items\n",
    "    \n",
    "    #真实item的总数\n",
    "    n_test_items += user_records_test.shape[0]\n",
    "\n",
    "#Precision & Recall\n",
    "precision = n_hits / (1.0*n_total_rec_items)\n",
    "recall = n_hits / (1.0*n_test_items)\n",
    "\n",
    "#覆盖度：推荐商品占总需要推荐商品的比例\n",
    "coverage = len(all_rec_items) / (1.0* n_items)\n",
    "\n",
    "#打分的均方误差\n",
    "rmse=np.sqrt(rss_test / df_triplet_test.shape[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001851851851851852"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00085"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17454545454545456"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9658669077094278"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上比较推荐数目10个和20个的数据值：\n",
    "推荐数目为10个：\n",
    "precision：0.0010893246187363835\n",
    "recall：0.00025\n",
    "coverage：0.10787878787878788\n",
    "rmse：0.9658669077094278\n",
    "\n",
    "推荐数目为20个：\n",
    "precision：0.001851851851851852\n",
    "recall：0.00085\n",
    "coverage：0.17454545454545456\n",
    "rmse：0.9658669077094278\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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